



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a      :  Optional[int]			     =			{
    'configuration_roformer': ['ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoFormerConfig', 'RoFormerOnnxConfig'],
    'tokenization_roformer': ['RoFormerTokenizer'],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a      :  str			     =			['RoFormerTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a      :  Any			     =			[
	    'ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'RoFormerForCausalLM',
	    'RoFormerForMaskedLM',
	    'RoFormerForMultipleChoice',
	    'RoFormerForQuestionAnswering',
	    'RoFormerForSequenceClassification',
	    'RoFormerForTokenClassification',
	    'RoFormerLayer',
	    'RoFormerModel',
	    'RoFormerPreTrainedModel',
	    'load_tf_weights_in_roformer',
	]


try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a      :  Union[str, Any]			     =			[
	    'TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TFRoFormerForCausalLM',
	    'TFRoFormerForMaskedLM',
	    'TFRoFormerForMultipleChoice',
	    'TFRoFormerForQuestionAnswering',
	    'TFRoFormerForSequenceClassification',
	    'TFRoFormerForTokenClassification',
	    'TFRoFormerLayer',
	    'TFRoFormerModel',
	    'TFRoFormerPreTrainedModel',
	]


try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a      :  Optional[int]			     =			[
	    'FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'FlaxRoFormerForMaskedLM',
	    'FlaxRoFormerForMultipleChoice',
	    'FlaxRoFormerForQuestionAnswering',
	    'FlaxRoFormerForSequenceClassification',
	    'FlaxRoFormerForTokenClassification',
	    'FlaxRoFormerModel',
	    'FlaxRoFormerPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
	from .tokenization_roformer import RoFormerTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_roformer_fast import RoFormerTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_roformer import (
		    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RoFormerForCausalLM,
		    RoFormerForMaskedLM,
		    RoFormerForMultipleChoice,
		    RoFormerForQuestionAnswering,
		    RoFormerForSequenceClassification,
		    RoFormerForTokenClassification,
		    RoFormerLayer,
		    RoFormerModel,
		    RoFormerPreTrainedModel,
		    load_tf_weights_in_roformer,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_roformer import (
		    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFRoFormerForCausalLM,
		    TFRoFormerForMaskedLM,
		    TFRoFormerForMultipleChoice,
		    TFRoFormerForQuestionAnswering,
		    TFRoFormerForSequenceClassification,
		    TFRoFormerForTokenClassification,
		    TFRoFormerLayer,
		    TFRoFormerModel,
		    TFRoFormerPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_roformer import (
		    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    FlaxRoFormerForMaskedLM,
		    FlaxRoFormerForMultipleChoice,
		    FlaxRoFormerForQuestionAnswering,
		    FlaxRoFormerForSequenceClassification,
		    FlaxRoFormerForTokenClassification,
		    FlaxRoFormerModel,
		    FlaxRoFormerPreTrainedModel,
		)


else:
	import sys

	a      :  int			     =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)











from typing import TYPE_CHECKING

from ...utils import _LazyModule


a      :  int			     =			{'tokenization_wav2vec2_phoneme': ['Wav2Vec2PhonemeCTCTokenizer']}


if TYPE_CHECKING:
	from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
	import sys

	a      :  int			     =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






